arXiv subjects

P. Sznajder

Publications and source records attributed to P. Sznajder.

30 records · Page 2Linked to original sources

arXiv2022

EpIC: novel Monte Carlo generator for exclusive processes

We present the EpIC Monte Carlo event generator for exclusive processes sensitive to generalised parton distributions. EpIC utilises the PARTONS framework, which provides a flexible software architecture and a variety of modelling options for the partonic description of the nucleon. The generator offers a comprehensive set of features, including multi-channel capabilities and radiative corrections. It may be used both in analyses of experimental data, as well as in impact studies, especially for future electron-ion colliders.

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arXiv2021

Artificial neural network modelling of generalised parton distributions

We discuss the use of machine learning techniques in effectively nonparametric modelling of generalised parton distributions (GPDs) in view of their future extraction from experimental data. Current parameterisations of GPDs suffer from model dependency that lessens their impact on phenomenology and brings unknown systematics to the estimation of quantities like Mellin moments. The new strategy presented in this study allows to describe GPDs in a way fulfilling theory-driven constraints, keeping model dependency to a minimum. Getting a better grip on the control of systematic effects, our work will help the GPD phenomenology to achieve its maturity in the precision era commenced by the new generation of experiments.

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arXiv2021

Shadow generalized parton distributions: a practical approach to the deconvolution problem of DVCS

Deeply virtual Compton scattering (DVCS) attracts a lot of interest due to its sensitivity to generalized parton distributions (GPDs) which provide a rich access to the partonic structure of hadrons. However, the practical extraction of GPDs for this channel requires a deconvolution procedure, whose feasibility has been disputed. We provide a practical approach to this problem based on a next-to-leading order analysis and a careful study of evolution effects, by exhibiting shadow GPDs with arbitrarily small imprints on DVCS observables at current and future experimental facilities. This shows that DVCS alone will not allow for a model independent extraction of GPDs and a multi-channel analysis is required for this purpose.

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arXiv2021

What do DVCS data tell us about TCS observables?

Deeply virtual Compton scattering (DVCS) and timelike Compton scattering (TCS) leading twist amplitudes are intimately related thanks to their analytic properties as a function of $Q^2$. We exploit this feature to use Compton form factors previously extracted from available DVCS data and derive data-driven predictions for TCS observables to be measured in near future experiments. Our results quantitatively illustrate the complementarity of DVCS and TCS experiments.

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arXiv2021

Impact of a positron beam at JLab on an unbiased determination of DVCS Compton Form Factors

The impact of potential future measurements of beam charge asymmetries on the current knowledge of Compton form factors is evaluated. Elaborating on the results of a global neural network fit to deeply virtual Compton scattering data, a Bayesian reweighting analysis quantifies the improved determination of the real part of the Compton form factor $\mathcal{H}$. Such an improvement is particularly relevant for the experimental determination of the proton mechanical properties or for universality studies of generalized parton distributions through analyses of virtual Compton scattering in both spacelike and timelike regions.

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arXiv2021

The deconvolution problem of deeply virtual Compton scattering

Generalised parton distributions are instrumental to study both the three-dimensional structure and the energy-momentum tensor of the nucleon, and motivate numerous experimental programmes involving hard exclusive measurements. Based on a next-to-leading order analysis and a careful study of evolution effects, we exhibit non-trivial generalised parton distributions with arbitrarily small imprints on deeply virtual Compton scattering observables. This means that in practice the reconstruction of generalised parton distributions from measurements, known as the deconvolution problem, does not possess a unique solution for this channel. In this Letter we discuss the consequences on the extraction of generalised parton distributions from data and advocate for a multi-channel analysis.

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arXiv2021

Phenomenological assessment of proton mechanical properties from deeply virtual Compton scattering

A unique feature of generalised parton distributions is their relation to the QCD energy-momentum tensor. In particular, they provide access to the mechanical properties of the proton i.e. the distributions of pressure and shear stress induced by its quark and gluon structure. In principle the pressure distribution can be experimentally determined in a model-independent way from a dispersive analysis of deeply virtual Compton scattering data through the measurement of the subtraction constant. In practice the kinematic coverage and accuracy of existing experimental data make this endeavour a challenge. Elaborating on recent global fits of deeply virtual Compton scattering measurements using artificial neural networks, our analysis presents the current knowledge on this subtraction constant and assesses the impact of the most frequent systematic assumptions made in this field of research. This study will pave the way for future works when more precise data will become available, e.g. obtained in the foreseen electron-ion colliders EIC and EIcC.

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arXiv2019

Data-driven study of timelike Compton scattering

In the framework of collinear QCD factorization, the leading twist scattering amplitudes for deeply virtual Compton scattering (DVCS) and timelike Compton scattering (TCS) are intimately related thanks to analytic properties of leading and next-to-leading order amplitudes. We exploit this welcome feature to make data-driven predictions for TCS observables to be measured in near future experiments. Using a recent extraction of DVCS Compton form factors from most of the existing experimental data for that process, we derive TCS amplitudes and calculate TCS observables only assuming leading-twist dominance. Artificial neural network techniques are used for an essential reduction of model dependency, while a careful propagation of experimental uncertainties is achieved with replica methods. Our analysis allows for stringent tests of the leading twist dominance of DVCS and TCS amplitudes. Moreover, this study helps to understand quantitatively the complementarity of DVCS and TCS measurements to test the universality of generalized parton distributions, which is crucial e.g. to perform the nucleon tomography.

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arXiv2019

Unbiased determination of DVCS Compton Form Factors

The extraction of Compton Form Factors (CFFs) in a global analysis of almost all Deeply Virtual Compton Scattering (DVCS) proton data is presented. The extracted quantities are DVCS sub-amplitudes and the most basic observables which are unambiguously accessible from this process. The parameterizations of CFFs are constructed utilizing the artificial neural network technique allowing for an important reduction of model dependency. The analysis consists of such elements as feasibility studies, training of neural networks with the genetic algorithm and a careful regularization to avoid over-fitting. The propagation of experimental uncertainties to extracted quantities is done with the replica method. The resulting parameterizations of CFFs are used to determine the subtraction constant through dispersion relations. The analysis is done within the PARTONS framework.

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arXiv2018

Border and skewness functions from a leading order fit to DVCS data

We propose new parameterizations for the border and skewness functions appearing in the description of 3D nucleon structure in the language of Generalized Parton Distributions (GPDs). These parameterizations are constructed in a way to fulfill the basic properties of GPDs, like their reduction to Parton Density Functions and Elastic Form Factors. They also rely on the power behavior of GPDs in the $x \to 1$ limit and the propounded analyticity property of Mellin moments of GPDs. We evaluate Compton Form Factors (CFFs), the sub-amplitudes of the Deeply Virtual Compton Scattering (DVCS) process, at the leading order and leading twist accuracy. We constrain the restricted number of free parameters of these new parameterizations in a global CFF analysis of almost all existing proton DVCS measurements. The fit is performed within the PARTONS framework, being the modern tool for generic GPD studies. A distinctive feature of this CFF fit is the careful propagation of uncertainties based on the replica method. The fit results genuinely permit nucleon tomography and may give some insight into the distribution of forces acting on partons.

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arXiv2018

PARTONS: PARtonic Tomography Of Nucleon Software. A computing framework for the phenomenology of Generalized Parton Distributions

We describe the architecture and functionalities of a C++ software framework, coined PARTONS, dedicated to the phenomenology of Generalized Parton Distributions. These distributions describe the three-dimensional structure of hadrons in terms of quarks and gluons, and can be accessed in deeply exclusive lepto- or photo-production of mesons or photons. PARTONS provides a necessary bridge between models of Generalized Parton Distributions and experimental data collected in various exclusive production channels. We outline the specification of the PARTONS framework in terms of practical needs, physical content and numerical capacity. This framework will be useful for physicists - theorists or experimentalists - not only to develop new models, but also to interpret existing measurements and even design new experiments.

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arXiv2012

HEPGEN - generator for hard exclusive leptoproduction

HEPGEN is a generator of Monte Carlo events, which is dedicated to studies of hard exclusive single photon or meson production processes at the COMPASS experiment kinematic domain. In addition HEPGEN allows to generate also single photon or meson production accompanied by the diffractive dissociation of the nucleon, which is one of the main sources of background in analyses of exclusive processes.

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